학술논문

Revealing the Evolution of Netflix Recommender Systems
Document Type
Conference
Source
2024 11th International Conference on Computing for Sustainable Global Development (INDIACom) Computing for Sustainable Global Development (INDIACom), 2024 11th International Conference on. :83-86 Feb, 2024
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
General Topics for Engineers
Geoscience
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Industries
Buildings
Recommender systems
Business
Recommender System
Netflix
Machine Learning
Analysis
Issues and Challenges
Language
Abstract
Recommender System (RS) has profoundly impacted the business of Netflix from DVD-by-mail to the streaming industry. Netflix has undergone a significant evolution since its inception. The continuous development over the years with an increase in revenue is embarking for many other OTTs and is a study of interest. This development was given a push with the plethora of research in the field of RS which came with the launch of the Netflix Prize in the year 2006. The competition highlighted the importance of RS, provided various dimensions of research, and increased the interest within the research community for the area. In this paper, the overall evolution of Netflix’s recommendation algorithm has been discussed. Further, implemented approaches to date, revenue analysis with changing strategies, and issues in current approaches have been briefly described.